← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.428 · 记录 #1
Experience-driven Multi-turn Reinforcement Learning for GUI Agents
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:02
摘要
GUI agents have demonstrated remarkable progress in automating complex user interface interactions. However, training such agents for long-horizon tasks remains challenging. Single-turn reinforcement learning conditions on expert histories during training but self-generated histories during deployment, causing distribution mismatch. Online multi-turn methods eliminate this gap via environment interaction but suffer from sparse rewards and prohibitive costs. We propose \underline{\textbf{E}}xperience-driven \underline{\textbf{M}}ulti-turn \underline{\textbf{P}}olicy \underline{\textbf{O}}ptimization (\textbf{EMPO}), which leverages expert trajectories as environment experiences for on-policy multi-turn training. The agent constructs self-generated history throughout rollouts; when actions match expert experiences, the trajectory provides valid state transitions, and a Patch Module recovers mismatched steps to maintain on-policy rollouts. EMPO further incorporates discounted future rewards and dual-level advantage estimation to capture long-horizon dependencies. We also propose \textbf{AndroidControl-Real}, an evaluation metric strongly correlated with real-world performance (R^2=0.934). With only 1K public trajectories as RL experiences, our method achieves substantial gains over the base model (e.g., +12.0% on AndroidWorld and +23.8% on AITW) and achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o, demonstrating better generalization than prior single-turn RL approaches. Code available: https://anonymous.4open.science/r/UI-S1-0DAF.
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记录 #1 · 2026-07 · 最新观测版本
Experience-driven Multi-turn Reinforcement Learning for GUI Agents
GUI agents have demonstrated remarkable progress in automating complex user interface interactions. However, training such agents for long-horizon tasks remains challenging. Single-turn reinforcement learning conditions on expert histories during training but self-generated histories during deployment, causing distribution mismatch. Online multi-turn methods eliminate this gap via environment interaction but suffer from sparse rewards and prohibitive costs. We propose \underline{\textbf{E}}xperience-driven \underline{\textbf{M}}ulti-turn \underline{\textbf{P}}olicy \underline{\textbf{O}}ptimization (\textbf{EMPO}), which leverages expert trajectories as environment experiences for on-policy multi-turn training. The agent constructs self-generated history throughout rollouts; when actions match expert experiences, the trajectory provides valid state transitions, and a Patch Module recovers mismatched steps to maintain on-policy rollouts. EMPO further incorporates discounted future rewards and dual-level advantage estimation to capture long-horizon dependencies. We also propose \textbf{AndroidControl-Real}, an evaluation metric strongly correlated with real-world performance (R^2=0.934). With only 1K public trajectories as RL experiences, our method achieves substantial gains over the base model (e.g., +12.0% on AndroidWorld and +23.8% on AITW) and achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o, demonstrating better generalization than prior single-turn RL approaches. Code available: https://anonymous.4open.science/r/UI-S1-0DAF.
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